Collaborative Research: Integration of Geographic Complexity and Dynamics into Geographic Information Systems

虽然地理世界的无限复杂性和动态性早已在地理信息科学（GIScience）文献中得到认可，但当前的地理信息系统（GIS）技术尚未纳入能够充分处理地理复杂性和动态性的数据模型、查询功能或分析工具。 本研究计画旨在将这些特徴整合至地理资讯系统的资料模型、查询与分析中。 这种集成将为下一代GIS技术奠定基础，进一步增强GIS对科学理解和发现地理世界的支持。 为了实现这一整合，调查人员将研究地理复杂性和动态。 基本的前提是，地理概念化需要超越地理世界的单独的领域和对象为基础的观点。 研究人员将专注于地理复杂性，这种复杂性来自于不同时空尺度上嵌入现象和关系中的场和对象的交织特性。他们将考虑反映在空间和时间的传播和演化的地理动态，如拉格朗日（专注于流动的静止作用）或哈密顿（专注于质量粒子的运动）动力学分析。 基于领域或对象的概念化本身无法捕捉复杂性和动态性，这对准确表示地理至关重要。 研究人员将整合两个额外的地理世界视图：对象的场（o-fields）和场的对象（f-objects），以整合地理复杂性和动态性。 因此，他们希望将双重地理概念化扩展到对象的光谱，f对象，o场和场，尺度和分辨率是允许视角沿着光谱转移的函数。 随着地理概念化的频谱，他们将开发一个数据模型，将地理复杂性和动态与不确定性，制定查询和分析功能，并建立一个原型系统的概念proof.The合作项目汇集了来自俄克拉荷马州，加州大学圣巴巴拉分校和犹他州大学的研究人员，以扩大他们的工作在地理空间数据建模。 在不同的企业中，他们研究了在地理表示中使用组合字段和对象，并证明这种组合有效地扩展了地理表示，以包含更丰富，更复杂的地理语义。 该研究项目的核心是基于地理复杂性和动态的GIS数据建模的想法，作为建立在数据捕获方式基础上的传统数据模型的替代方案。 该研究项目承诺更广泛，更全面地检查与领域和对象的整合以及地理复杂性和动态表示的整体理论的发展有关的问题。 这种GIS数据建模的新方法将静态表示扩展到复杂和动态的世界视图，从而增强了GIS技术，使其更适合于科学研究。

While the infinite complexity and dynamics of geographic worlds have long been recognized in the geographic information science (GIScience) literature, current geographic information systems (GIS) technology has not yet incorporated data models, query functions, or analytic tools that can adequately handle geographic complexity and dynamics. This research project aims to integrate these features into GIS data models, query, and analysis. Such integration will lay a foundation for the next generations of GIS technology to further empower GIS support for scientific understanding and discovery of geographic worlds. To achieve this integration, the investigators will examine geographic complexity and dynamics. The basic premise is that geographic conceptualizations need to go beyond separate field- and object-based views of geographic worlds. The investigators will focus on geographic complexity that arises from the interwoven properties of fields and objects embedded in phenomena and relationships at different spatial and temporal scales. They will consider geographic dynamics that reflect on propagation and evolution in space and time as analyzed by Lagrangian (focusing on the stationary action of flows) or Hamiltonian (focusing on the motion of a particle of mass) dynamics. Field- or object-based conceptualization alone cannot capture complexity and dynamics critical to an accurate representation of geography. The investigators will incorporate two additional views of geographic worlds: fields of objects (o-fields) and objects of fields (f-objects) to incorporate geographic complexity and dynamics. They therefore expect to extend the dual geographic conceptualization to a spectrum of objects, f-objects, o-fields, and fields, with scale and resolution are as functions that allow a shift in perspective along the spectrum. With the spectrum of geographic conceptualizations, they will develop a data model that incorporates geographic complexity and dynamics with uncertainty, formulates queries and analytical functions, and builds a prototype system for proof of concepts.The collaborative project brings together researchers from the University of Oklahoma, University of California-Santa Barbara, and University of Utah to expand on their work on geospatial data modeling. In separate ventures, they have examined the use of combined fields and objects on geographic representation and demonstrated that such combination effectively extends geographic representation to incorporate much richer, more complex geographic semantics. Central to the research project is the idea of modeling GIS data based on geographic complexity and dynamics, as an alternative to the conventional data models that are built upon how data are captured. The research project promises a broader, more comprehensive inspection of issues related to the integration of fields and objects and the development of a holistic theory of the representation of geographic complexity and dynamics. This new approach to GIS data modeling extends static representation to a complex and dynamic view of the world and thus enhances GIS technology to be better suited for scientific research.